Gated QKAN-FWP combines fast weight programming with quantum-inspired Kolmogorov-Arnold networks via single-qubit DARUAN activations and gated updates to deliver a 12.5k-parameter model that outperforms larger classical RNNs on long-horizon solar forecasting while running on NISQ devices.
Effect of data encoding on the expressive power of variational quantum-machine-learning models
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An efficient classical algorithm reduces the NTK average for Clifford-Pauli quantum neural networks to four discrete Clifford gates, enabling Gaussian-process simulation of wide trained networks and ruling out quantum advantage for this class.
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Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning
Gated QKAN-FWP combines fast weight programming with quantum-inspired Kolmogorov-Arnold networks via single-qubit DARUAN activations and gated updates to deliver a 12.5k-parameter model that outperforms larger classical RNNs on long-horizon solar forecasting while running on NISQ devices.
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Efficient classical computation of the neural tangent kernel of quantum neural networks
An efficient classical algorithm reduces the NTK average for Clifford-Pauli quantum neural networks to four discrete Clifford gates, enabling Gaussian-process simulation of wide trained networks and ruling out quantum advantage for this class.